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      + The aggregation processes are continuously running and apply vocabularies as they are in a given + moment of time. + It could be the case that a vocabulary changes after the aggregation of one data source has + finished, + thus the aggregated content does not reflect the current status of the controlled vocabularies. +

      + In addition, the integration of ScholeXplorer and DOIBooost and some enrichment processes + applied + on the raw + and on the de-duplicated graph may introduce values that do not comply with the current status + of + the OpenAIRE controlled vocabularies. + For these reasons, we included a final step of cleansing at the end of the workflow + materialisation. + The output of the final cleansing step is the final version of the OpenAIRE Research Graph. +

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    • + Enrichment +
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      + The OpenAIRE Research Graph is enriched by links mined by OpenAIRE’s full-text mining + algorithms + that scan the plaintexts of publications for funding information, references to datasets, + software URIs, accession numbers of bioetities, and EPO patent mentions. + Custom mining modules also link research objects to specific research communities, initiatives + and infrastructures. + In addition, other inference modules provide content-based document classification, document + similarity, citation matching, and author affiliation matching. +

      + Project mining + in OpenAIRE text mines the full-texts of publications in order to extract matches to funding + project codes/IDs. + The mining algorithm works by utilising + (i) the grant identifier, and + (ii) the project acronym (if available) of each project. + The mining algorithm: + (1) Preprocesses/normalizes the full-texts using several functions, which depend on the + characteristics of each funder (i.e., the format of the grant identifiers), such as stopword + and/or punctuation removal, tokenization, stemming, converting to lowercase; then + (2) String matching of grant identifiers against the normalized text is done using database + techniques; and + (3) The results are validated and cleaned using the context near the match by looking at the + context around the matched ID for relevant metadata and positive or negative words/phrases, in + order to calculate a confidence value for each publication-->project link. + A confidence threshold is set to optimise high accuracy while minimising false positives, such + as matches with page or report numbers, post/zip codes, parts of telephone numbers, DOIs or + URLs, accession numbers. + The algorithm also applies rules for disambiguating results, as different funders can share + identical project IDs; for example, grant number 633172 could refer to H2020 project EuroMix + but + also to Australian-funded NHMRC project “Brain activity (EEG) analysis and brain imaging + techniques to measure the neurobiological effects of sleep apnea”. + Project mining works very well and was the first Text & Data Mining (TDM) service of OpenAIRE. + Performance results vary from funder to funder but precision is higher than 98% for all + funders + and 99.5% for EC projects. + Recall is higher than 95% (99% for EC projects), when projects are properly acknowledged using + project/grant IDs. +

      + Dataset extraction + runs on publications full-texts as described in “High pass text-filtering for Citation + matching”, TPDL 2017[1]. + In particular, we search for citations to datasets using their DOIs, titles and other metadata + (i.e., dates, creator names, publishers, etc.). + We extract parts of the text which look like citations and search for datasets using database + join and pattern matching techniques. + Based on the experiments described in the paper, precision of the dataset extraction module is + 98.5% and recall is 97.4% but it is also probably overestimated since it does not take into + account corruptions that may take place during pdf to text extraction. + It is calculated on the extracted full-texts of small samples from PubMed and arXiv. +

      + Software extraction + runs also on parts of the text which look like citations. + We search the citations for links to software in open software repositories, specifically + github, sourceforge, bitbucket and the google code archive. + After that, we search for links that are included in Software Heritage (SH, + https://www.softwareheritage.org) and return the permanent URL that SH provides for each + software project. + We also enrich this content with user names, titles and descriptions of the software projects + using web mining techniques. + Since software mining is based on URL matching, our precision is 100% (we return a software + link + only if we find it in the text and there is no need to disambiguate). + As for recall rate, this is not calculable for this mining task. + Although we apply all the necessary normalizations to the URLs in order to overcome usual + issues + (e.g., http or https, existence of www or not, lower/upper case), we do not calculate cases + where a software is mentioned using its name and not by a link from the supported software + repositories. +

      + For the extraction of bio-entities, we focus on Protein Data + Bank (PDB) entries. + We have downloaded the database with PDB codes and we update it regularly. + We search through the whole publication’s full-text for references to PDB codes. + We apply disambiguation rules (e.g., there are PDB codes that are the same as antibody codes + or + other issues) so that we return valid results. + Current precision is 98%. + Although it's risky to mention recall rates since these are usually overestimated, we have + calculated a recall rate of 98% using small samples from pubmed publications. + Moreover, our technique is able to identify about 30% more links to proteins than the ones + that + are tagged in Pubmed xmls. +

      + Other text-mining modules include mining for links to EPO + patents, or custom mining modules for linking research objects to specific research + communities, + initiatives and infrastructures, e.g. COVID-19 mining module. + Apart from text-mining modules, OpenAIRE also provides a document classification service that + employs analysis of free text stemming from the abstracts of the publications. + The purpose of applying a document classification module is to assign a scientific text one or + more predefined content classes. + In OpenAIRE, the currently used taxonomies are arXiv, MeSH (Medical Subject Headings), ACM and + DDC (Dewey Decimal Classification, or Dewey Decimal System). +

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      + [1] Foufoulas, Y., Stamatogiannakis, L., Dimitropoulos, H., & Ioannidis, Y. (2017, September). + High-Pass Text Filtering for Citation Matching. + In International Conference on Theory and Practice of Digital Libraries (pp. 355-366). + Springer, + Cham. +
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    • + Enrichment +
      + The Deduction process (also known as “bulk tagging”) enriches each record with new information + that + can be derived from the existing property values. +

      + As of September 2020, three procedures are in place to relate a research product to a research + initiative, infrastructure (RI) or community (RC) based on: +
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      • subjects (2.7M results tagged)
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      • the data source it comes from (250K results tagged)
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      + The list of subjects, Zenodo communities and data sources used to enrich the products are defined + by + the managers of the community gateway or infrastructure monitoring dashboard associated with the + RC/RI. +
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      + This process “propagates” properties and links from one product to another if between the two + there is a “strong” semantic relationship. +

      + As of September 2020, the following procedures are in place: +
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      • + Propagation of the property “country” to results from institutional repositories: + e.g. publication collected from an institutional repository maintained by an italian + university will be enriched with the property “country = IT”. +
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      • + Propagation of links to projects: e.g. publication linked to project P “is supplemented + by” + a dataset D. + Dataset D will get the link to project P. + The relationships considered for this procedure are “isSupplementedBy” and “supplements”. +
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      • + Propagation of related community/infrastructure/initiative from organizations to products + via affiliation relationships: e.g. a publication with an author affiliated with + organization O. + The manager of the community gateway C declared that the outputs of O are all relevant for + his/her community C. + The publication is tagged as relevant for C. +
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      • + Propagation of related community/infrastructure/initiative to related products: e.g. + publication associated to community C is supplemented by a dataset D. + Dataset D will get the association to C. + The relationships considered for this procedure are “isSupplementedBy” and “supplements”. +
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      • + Propagation of ORCID identifiers to related products, if the products have the same + authors: + e.g. publication has ORCID for its authors and is supplemented by a dataset D. Dataset D + has + the same authors as the publication. Authors of D are enriched with the ORCIDs available + in + the publication. + The relationships considered for this procedure are “isSupplementedBy” and “supplements”. +
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    - The final version of the OpenAIRE Research Graph is indexed on a Solr server that is used by the - OpenAIRE portals (EXPLORE, CONNECT, PROVIDE) and APIs, the latter adopted by several third-party - applications and organizations, such as: -

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    • - EOSC - --The OpenAIRE Research Graph APIs and Portals will offer to the EOSC an Open Science Resource - Catalogue, keeping an up to date map of all research results (publications, datasets, software), - services, organizations, projects, funders in Europe and beyond. -
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    • - DSpace & EPrints - repositories can install the OpenAIRE plugin to expose OpenAIRE compliant metadata records via their - OAI-PMH endpoint and offer to researchers the possibility to link their depositions to the funding - project, by selecting it from the list of project provided by OpenAIRE -
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    • - EC participant portal (Sygma - System for Grant Management) - uses the OpenAIRE API in the “Continuous Reporting” section. - Sygma automatically fetches from the OpenAIRE Search API the list of publications and datasets in - the - OpenAIRE Research Graph that are linked to the project. - The user can select the research products from the list and easily compile the continuous reporting - data of the project. -
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    - The OpenAIRE Research Graph is also processed by a pipeline for extracting the statistics and - producing - the charts for funders, research initiative, infrastructures, and policy makers that you can see on - MONITOR. - Based on the information available on the graph, OpenAIRE provides a set of indicators for monitoring - the funding and research impact and the uptake of Open Science publishing practices, - such as Open Access publishing of publications and datasets, availability of interlinks between - research - products, availability of post-print versions in institutional or thematic Open Access repositories, - etc. -

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    + The final version of the OpenAIRE Research Graph is indexed on a Solr server that is used by the + OpenAIRE portals (EXPLORE, CONNECT, PROVIDE) and APIs, the latter adopted by several third-party + applications and organizations, such as: +

    +
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    • + EOSC + --The OpenAIRE Research Graph APIs and Portals will offer to the EOSC an Open Science Resource + Catalogue, keeping an up to date map of all research results (publications, datasets, software), + services, organizations, projects, funders in Europe and beyond. +
      • +
    • + DSpace & EPrints + repositories can install the OpenAIRE plugin to expose OpenAIRE compliant metadata records via their + OAI-PMH endpoint and offer to researchers the possibility to link their depositions to the funding + project, by selecting it from the list of project provided by OpenAIRE +
      • +
    • + EC participant portal (Sygma - System for Grant Management) + uses the OpenAIRE API in the “Continuous Reporting” section. + Sygma automatically fetches from the OpenAIRE Search API the list of publications and datasets in + the + OpenAIRE Research Graph that are linked to the project. + The user can select the research products from the list and easily compile the continuous reporting + data of the project. +
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    + + Stats Analysis +
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    + The OpenAIRE Research Graph is also processed by a pipeline for extracting the statistics and + producing + the charts for funders, research initiative, infrastructures, and policy makers that you can see on + MONITOR. + Based on the information available on the graph, OpenAIRE provides a set of indicators for monitoring + the funding and research impact and the uptake of Open Science publishing practices, + such as Open Access publishing of publications and datasets, availability of interlinks between + research + products, availability of post-print versions in institutional or thematic Open Access repositories, + etc. +

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    References